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Local Binary Pattern (LBP) histograms have attained much attention in face image analysis. They have been successfully used in face detection, recognition, verification, facial expression recognition etc. The models for face description have been based on LBP histograms computed within small image blocks. In this work we propose a novel, spatially more precise model, based on kernel density estimation of local LBP distributions. In the experiments we show that this model produces significantly better performance in the face verification task than the earlier models. Furthermore, we show that the use of weighted information fusion from individual pixels based on a linear support vector machine provides with further improvements in performance.

Pixelwise Local Binary Pattern Models of Faces Using Kernel Density Estimation